Target Allocation of WSN Based on Parallel Chaotic Elite Quantum-Inspired Evolutionary Algorithm

The target allocation problem is one of the important challenges in WSNs as sensor nodes have limited sensing and communication capabilities. In the target allocation problem, a set of targets is selected for each sensor to improve the monitoring quality as well as the energy efficiency. However, the target allocation problem is a combinatorial optimization problem, and the computational complexity is too high to consider all combinations for practical implementation. In this paper, we propose a novel Parallel Chaotic Elite Quantum-Inspired Evolutionary Algorithm (PCEQEA) for target allocation problem in WSNs. The PCEQEA combines the advantages of elite genetic algorithm and quantum inspired evolutionary algorithm. It achieves high parallel search performance and fast convergence to global optimum solution. Simulation results demonstrate that proposed PCEQEA improves WSN detection coverage by detecting more targets than other existing schemes.